Quick start#
The RssMaker class in autoplex is the core interface for creating ML-RSS potential models from scratch. It accepts customizable parameters that control key aspects of the RSS process, including:
Randomized structure generation
Generate diverse initial structures for broader configurational space exploration.Sampling strategies
Customize methods for selecting configurations based on energy and structure diversity.DFT labeling
Perform single-point DFT calculations to provide accurate energy and force labels for training.Data preprocessing
Include steps like data regularization and filtering to improve model performance.Potential fitting
Perform machine learning potential fitting with flexible hyperparameter tuning.Iterative loop
Continuously refine the potential model through iterative RSS cycles.
Parameters can be specified either through a YAML configuration file or as direct arguments in the make method.
Running the workflow with a YAML configuration file#
Recommendation: This is currently our recommended approach for setting up and managing RSS workflows.
The RSS workflow can be initiated using a custom YAML configuration file. A comprehensive list of parameters, including default settings and modifiable options, is available in autoplex/auto/rss/rss_default_configuration.yaml. When creating a new YAML file, any specified keys will override the corresponding default values. To start a new workflow, pass the path to your YAML file as the config_file argument in the make method.
from autoplex.auto.rss.flows import RssMaker
from fireworks import LaunchPad
from jobflow import Flow
from jobflow.managers.fireworks import flow_to_workflow
rss_job = RssMaker(name="your workflow name").make(config_file='path/to/your/name.yaml')
wf = flow_to_workflow(rss_job)
lpad = LaunchPad.auto_load()
lpad.add_wf(wf)
The above code is based on FireWorks for job submission and management. You could also use jobflow-remote, in which case the code snippet would change as follows.
from autoplex.auto.rss.flows import RssMaker
from jobflow_remote import submit_flow
rss_job = RssMaker(name="your workflow name").make(config_file='path/to/your/name.yaml')
resources = {"nodes": N, "partition": "name", "qos": "name", "time": "8:00:00", "mail_user": "your_email", "mail_type": "ALL", "account": "your account"}
print(submit_flow(rss_job, worker="your worker", resources=resources, project="your project name"))
For details on setting up FireWorks, see FireWorks setup and for jobflow-remote, see jobflow-remote setup.
Running the workflow with direct parameter specification#
As an alternative to using a YAML configuration file, the RSS workflow can be initiated by directly specifying parameters in the make method. This approach is ideal for cases where only a few parameters need to be customized. You can override the default settings by passing them as keyword arguments, offering a more flexible and lightweight way to set up the workflow.
rss_job = RssMaker(name="your workflow name").make(tag='Si',
buildcell_options=[{'NFORM': '{1,3,5}'},
{'NFORM': '{2,4,6}'}],
hookean_repul=True,
hookean_paras={'(14, 14)': (100, 1.2)})
If you choose to use the direct parameter specification method, at a minimum, you must provide the following arguments:
tag: defines the system’s elements and stoichiometry (only for compounds).buildcell_options: controls the parameters for generating the initial randomized structures.hookean_repul: enables a strong repulsive force to avoid physically unrealistic structures.hookean_paras: specifies the Hookean repulsion parameters.
Recommendation: We strongly recommend enabling
hookean_repul, as it applies a strong repulsive force when the distance between two atoms falls below a certain threshold. This ensures that the generated structures are physically reasonable.
Note: If both a YAML file and direct parameter specifications are provided, any overlapping parameters will be overridden by the directly specified values.
Building RSS models with various ML potentials#
Currently, RssMaker supports GAP (Gaussian Approximation Potential), ACE (Atomic Cluster Expansion), and three graph-based network models including NequIP, M3GNet, and MACE. You can specify the desired model using the mlip_type argument and adjust relevant hyperparameters within the make method. Default and adjustable hyperparameters are available in autoplex/fitting/common/mlip-rss-defaults.json.
rss_job = RssMaker(name="your workflow name").make(tag='SiO2',
... # Other parameters here
mlip_type='MACE',
hidden_irreps="128x0e + 128x1o",
r_max=5.0)
Note: We primarily recommend the GAP-RSS model for now, as GAP has demonstrated great stability with small datasets. Other models have not been thoroughly explored yet. However, we encourage users to experiment with and test other individual models or combinations for potentially interesting results.
Resuming workflow from point of interruption#
To resume an interrupted RSS workflow, use the resume_from_previous_state argument, which accepts a dictionary containing the necessary state information. Additionally, ensure that train_from_scratch is set to False to enable resuming from the previous state. This way, you are allowed to continue the workflow from any previously saved state.
rss_job = RssMaker(name="your workflow name").make(tag='SiO2',
... # Other parameters here
train_from_scratch=False,
resume_from_previous_state={'test_error': 0.24,
'pre_database_dir': 'path/to/pre-existing/database',
'mlip_path': 'path/to/previous/MLIP-model',
'isolated_atom_energies': {8: -0.16613333, 14: -0.16438578},
})